This is a data set that includes the following variables:
| DH1_W_Responsible |
| DH2_W_Civilized |
| DH3_W_Moral |
| DH4_W_Polite |
| DH5_W_Childlike |
| DH6_W_Rational |
| DH7_W_Warm |
| DH8_W_Agentic |
| DH9_W_Refined |
| DH10_W_Lacking_Culture |
| DH11_W_Lacking_Self-restraint |
| DH12_W_Instinctual |
| DH13_W_Mature |
| DH14_W_Stoic |
| DH15_W_Emotionally_Responsive |
| DH16_W_Cold |
| DH17_W_Open |
| DH18_W_Rigid |
| DH19_W_Passive |
| DH20_W_Superficial |
| DH1_N_Responsible |
| DH2_N_Civilized |
| DH3_N_Moral |
| DH4_N_Polite |
| DH5_N_Childlike |
| DH6_N_Rational |
| DH7_N_Warm |
| DH8_N_Agentic |
| DH9_N_Refined |
| DH10_N_Lacking_Culture |
| DH11_N_Lacking_Self-restraint |
| DH12_N_Instinctual |
| DH13_N_Mature |
| DH14_N_Stoic |
| DH15_N_Emotionally_Responsive |
| DH16_N_Cold |
| DH17_N_Open |
| DH18_N_Rigid |
| DH19_N_Passive |
| DH20_N_Superficial |
| No_Columbus |
| Create_IPD |
| BorninUS |
| LengthState |
| FatherUSBorn |
| MotherUSBorn |
| PrimaryCaretaker |
| PrimaryEd |
| SecondCaretaker |
| SecondEdu |
| Religion |
| Religiosity |
| College |
| Year |
| Education |
| LibCon |
| SES |
| Race |
| StateNumeric |
| LongestRegion |
| SubjectNumber |
| White_Dehumanize |
| Native_Dehumanize |
| Age |
It’s primary focus is to look a bit more closely at the dehumaniztion variables and how they interact not only with one another, but with the dependent variables (Columbus Day/Indigenous Peoples’ Day (henceforth referred to as IPD)), as well as some of the demographic information gathered on the participants in this sample.
This sample comes from Study Two, so it is (if I’m not mistaken) a sample of participants gathered via mturk.
First, lets take a look at a descriptives table of our variables. Keep in mind that some of the variables are categorical and are more difficult to interpret in a descriptives table.
| Â | n | mean | sd | median | se |
|---|---|---|---|---|---|
| DH1_W_Responsible | 2811 | 61.24 | 21.82 | 62 | 0.4116 |
| DH2_W_Civilized | 2810 | 65.89 | 23.38 | 69 | 0.4411 |
| DH3_W_Moral | 2812 | 55.77 | 22.76 | 55 | 0.4292 |
| DH4_W_Polite | 2812 | 57.99 | 22.53 | 58 | 0.4248 |
| DH5_W_Childlike | 2809 | 40.76 | 24.49 | 43 | 0.462 |
| DH6_W_Rational | 2810 | 58.23 | 22.49 | 57 | 0.4243 |
| DH7_W_Warm | 2810 | 58.27 | 22.32 | 58 | 0.421 |
| DH8_W_Agentic | 2800 | 46.11 | 22.34 | 50 | 0.4222 |
| DH9_W_Refined | 2809 | 52.87 | 22.26 | 52 | 0.4199 |
| DH10_W_Lacking_Culture | 2808 | 46.34 | 27.71 | 50 | 0.523 |
| DH11_W_Lacking_Self-restraint | 2808 | 48.59 | 25.58 | 50 | 0.4828 |
| DH12_W_Instinctual | 2810 | 51.47 | 22.98 | 51 | 0.4335 |
| DH13_W_Mature | 2809 | 57.34 | 22.31 | 56 | 0.421 |
| DH14_W_Stoic | 2808 | 42.88 | 22.31 | 49 | 0.4209 |
| DH15_W_Emotionally_Responsive | 2812 | 59.98 | 22.77 | 60 | 0.4294 |
| DH16_W_Cold | 2810 | 44.74 | 23.99 | 49 | 0.4525 |
| DH17_W_Open | 2811 | 55.59 | 22.81 | 55 | 0.4302 |
| DH18_W_Rigid | 2807 | 49.6 | 23.27 | 50 | 0.4393 |
| DH19_W_Passive | 2810 | 45.16 | 23.21 | 49 | 0.4378 |
| DH20_W_Superficial | 2810 | 59.32 | 25.54 | 60 | 0.4818 |
| DH1_N_Responsible | 2807 | 63.86 | 21.06 | 64 | 0.3976 |
| DH2_N_Civilized | 2806 | 64.58 | 21.97 | 65 | 0.4147 |
| DH3_N_Moral | 2807 | 65.75 | 20.88 | 66 | 0.3941 |
| DH4_N_Polite | 2806 | 63.22 | 21.42 | 63 | 0.4044 |
| DH5_N_Childlike | 2802 | 29.22 | 22.26 | 25 | 0.4205 |
| DH6_N_Rational | 2807 | 61.61 | 21.17 | 61 | 0.3995 |
| DH7_N_Warm | 2804 | 61.05 | 22.07 | 60 | 0.4167 |
| DH8_N_Agentic | 2800 | 44.97 | 22.32 | 50 | 0.4219 |
| DH9_N_Refined | 2806 | 51.11 | 22.44 | 51 | 0.4235 |
| DH10_N_Lacking_Culture | 2804 | 23.9 | 23.15 | 17 | 0.4371 |
| DH11_N_Lacking_Self-restraint | 2805 | 34.44 | 23.39 | 33 | 0.4416 |
| DH12_N_Instinctual | 2807 | 61.3 | 23.48 | 61 | 0.4433 |
| DH13_N_Mature | 2806 | 65.15 | 20.69 | 65 | 0.3907 |
| DH14_N_Stoic | 2808 | 59.11 | 22.29 | 57 | 0.4206 |
| DH15_N_Emotionally_Responsive | 2806 | 54.38 | 24.29 | 53 | 0.4585 |
| DH16_N_Cold | 2805 | 35.19 | 22.8 | 35 | 0.4306 |
| DH17_N_Open | 2805 | 53.8 | 23.34 | 53 | 0.4406 |
| DH18_N_Rigid | 2807 | 45.41 | 23.89 | 50 | 0.451 |
| DH19_N_Passive | 2805 | 45.03 | 23.79 | 50 | 0.4492 |
| DH20_N_Superficial | 2805 | 30.41 | 22.87 | 27 | 0.4318 |
| No_Columbus | 2858 | 3.793 | 2.125 | 4 | 0.03976 |
| Create_IPD | 2858 | 4.481 | 1.956 | 4 | 0.03658 |
| BorninUS* | 2801 | 1.956 | 0.2041 | 2 | 0.003857 |
| LengthState | 2797 | 30.72 | 12.88 | 29 | 0.2435 |
| FatherUSBorn* | 2801 | 1.893 | 0.3352 | 2 | 0.006334 |
| MotherUSBorn* | 2801 | 1.882 | 0.3337 | 2 | 0.006306 |
| PrimaryCaretaker* | 2800 | 1.282 | 0.7601 | 1 | 0.01436 |
| PrimaryEd | 2801 | 2.988 | 1.225 | 3 | 0.02315 |
| SecondCaretaker* | 2801 | 2.828 | 1.114 | 3 | 0.02105 |
| SecondEdu | 2736 | 2.908 | 1.323 | 2 | 0.02529 |
| Religion* | 2799 | 2.45 | 1.674 | 2 | 0.03164 |
| Religiosity | 2800 | 3.632 | 2.235 | 4 | 0.04224 |
| College* | 2799 | 1.893 | 0.3094 | 2 | 0.005848 |
| Year | 300 | 3.363 | 1.631 | 3 | 0.09417 |
| Education | 2800 | 3.459 | 1.127 | 4 | 0.0213 |
| LibCon | 2799 | 4.401 | 1.713 | 4 | 0.03237 |
| SES* | 2801 | 1.538 | 0.4986 | 2 | 0.009422 |
| Race* | 2903 | 3.42 | 1.684 | 3 | 0.03125 |
| StateNumeric* | 2788 | 24.73 | 14.36 | 25 | 0.272 |
| LongestRegion* | 2789 | 4.425 | 2.104 | 5 | 0.03984 |
| SubjectNumber | 2903 | 1452 | 838.2 | 1452 | 15.56 |
| White_Dehumanize | 2793 | 52.93 | 12.03 | 53.25 | 0.2275 |
| Native_Dehumanize | 2792 | 50.67 | 11.38 | 51.05 | 0.2153 |
| Age | 2793 | 38.72 | 13.33 | 35 | 0.2523 |
One thing you’ll probably notice right away is that I’ve made some aggregate variables at the end of the data set that average across all Dehumanization measures for both whites and natives. Let’s take a look at the relationship between the two zoomed out a bit.
##
## Pearson's product-moment correlation
##
## data: dfdehumanDV$White_Dehumanize and dfdehumanDV$Native_Dehumanize
## t = 55.589, df = 2784, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7072142 0.7424398
## sample estimates:
## cor
## 0.7253014
At baseline the two seem to hang together pretty well. People seem to be rating Natives and Whites similarly among the measures of dehumanization that we have.
Before we move on, let’s take a gander at the dependent variables. Does there seem to be a relationship between dehumanization and Support for getting rid of Columbus Day?
## Warning: Removed 221 rows containing missing values (geom_point).
##
## Call:
## lm(formula = No_Columbus ~ Rating + Group + Rating:Group, data = dv4cor.lng)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2443 -1.7991 0.1583 2.1348 4.5126
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.244311 0.180457 29.061 < 2e-16 ***
## Rating -0.027582 0.003325 -8.296 < 2e-16 ***
## GroupNative_Dehumanize -1.296491 0.256706 -5.050 4.55e-07 ***
## Rating:GroupNative_Dehumanize 0.024315 0.004839 5.025 5.19e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.113 on 5581 degrees of freedom
## (221 observations deleted due to missingness)
## Multiple R-squared: 0.01233, Adjusted R-squared: 0.0118
## F-statistic: 23.23 on 3 and 5581 DF, p-value: 6.143e-15
**Keep in mind while looking at this graph that low scores on the dehumanization measure mean that participants are dehumanizing the group more.
What about between dehumanization and Support for IPD?
## Warning: Removed 221 rows containing missing values (geom_point).
##
## Call:
## lm(formula = Create_IPD ~ Rating + Group + Rating:Group, data = dv5cor.lng)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1305 -1.3811 -0.2253 1.5698 3.0665
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.130533 0.166784 30.762 < 2e-16 ***
## Rating -0.012417 0.003073 -4.041 5.40e-05 ***
## GroupNative_Dehumanize -1.142140 0.237256 -4.814 1.52e-06 ***
## Rating:GroupNative_Dehumanize 0.021991 0.004472 4.917 9.03e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.953 on 5581 degrees of freedom
## (221 observations deleted due to missingness)
## Multiple R-squared: 0.004461, Adjusted R-squared: 0.003926
## F-statistic: 8.337 on 3 and 5581 DF, p-value: 1.577e-05
We can also look at both of these groups side by side:
## Warning: Removed 221 rows containing missing values (geom_point).
and…
## Warning: Removed 221 rows containing missing values (geom_point).
Whe can see more clearly that significant interaction when they are side by side. Attitudes toward white more strongly predicts the relationship between dehumanization and support for removing Columbus day.
Interestingly, the direction of that relationship changes when we’re talking about IPD. If we create a model that looks at this interaction with both ratings of support and dehumanization centered at the mean we see that this change in direction is significant (p = .005)
##
## Call:
## lm(formula = Native_Dehumanize ~ Rating + Holiday_Support + Rating:Holiday_Support,
## data = dv4and5cor.lng.cen)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.489 -4.395 0.429 5.979 50.128
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.10887 0.21830 -0.499 0.61799
## Rating 0.32336 0.10994 2.941 0.00328 **
## Holiday_SupportNo_Columbus 0.07569 0.30859 0.245 0.80626
## Rating:Holiday_SupportNo_Columbus -0.41695 0.14945 -2.790 0.00529 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.37 on 5580 degrees of freedom
## (222 observations deleted due to missingness)
## Multiple R-squared: 0.001701, Adjusted R-squared: 0.001164
## F-statistic: 3.169 on 3 and 5580 DF, p-value: 0.02334
**For some reason in the markdown document the "Rating" coefficient is significant. In the code it is not (p=.12).
Let’s dig into some of the demographic information in this subset of the data.
First let’s look at the frequency of our different racial groups
| Asian | Black | White | Latino | Middle Eastern | Native | Other | Multiracial |
|---|---|---|---|---|---|---|---|
| 139 | 224 | 2097 | 106 | 4 | 17 | 24 | 292 |
Since our data skews toward Asian, Black, White, and Latino let’s use these groups in future analysis.
First off, let’s look at how our different racial groups answered the question about Columbus Day.
## -------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## -------------------------------------------------------------------------
##
## Attaching package: 'plyr'
## The following objects are masked from 'package:Hmisc':
##
## is.discrete, summarize
## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
## The following object is masked from 'package:purrr':
##
## compact
Next let’s see how these groups answered the question about Indigenous Peoples’ Day
For the next set of graphs let’s revisit the question of dehumanization. What we’re interested in here is whether or not responses to the questions of dehumanization differed by racial group.
A little later on we’ll look specifically at questions around being civilized and childlike and whether these things predict responses to our DV questions and whether they interact with race at all.
| Race | N | White_Dehumanize | sd | se | ci |
|---|---|---|---|---|---|
| Asian | 139 | 51.85 | 11.17 | 0.9476 | 1.874 |
| Black | 222 | 48.73 | 14.96 | 1.004 | 1.979 |
| White | 2082 | 53.57 | 11.54 | 0.2529 | 0.4959 |
| Latino | 105 | 53.65 | 12.13 | 1.183 | 2.347 |
## Df Sum Sq Mean Sq F value Pr(>F)
## Race 3 4952 1650.8 11.7 1.31e-07 ***
## Residuals 2544 359044 141.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 18 observations deleted due to missingness
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = White_Dehumanize ~ Race, data = dfdehumanDV.race)
##
## $Race
## diff lwr upr p adj
## Black-Asian -3.12266025 -6.4259093 0.1805888 0.0717501
## White-Asian 1.72137161 -0.9540863 4.3968295 0.3485664
## Latino-Asian 1.80477218 -2.1440217 5.7535661 0.6427737
## White-Black 4.84403186 2.6877973 7.0002664 0.0000001
## Latino-Black 4.92743243 1.3102173 8.5446476 0.0026465
## Latino-White 0.08340058 -2.9712461 3.1380473 0.9998760
This analysis is done via One way ANOVA using Dehumanization of Whites as the DV
Now let’s look at those same tables and bar graphs for Natives:
| Race | N | Native_Dehumanize | sd | se | ci |
|---|---|---|---|---|---|
| Asian | 139 | 49.91 | 11.02 | 0.935 | 1.849 |
| Black | 220 | 50.31 | 11.92 | 0.8035 | 1.584 |
| White | 2084 | 50.61 | 11.22 | 0.2458 | 0.482 |
| Latino | 106 | 54.06 | 12.19 | 1.184 | 2.348 |
## Df Sum Sq Mean Sq F value Pr(>F)
## Race 3 1332 443.9 3.468 0.0156 *
## Residuals 2545 325757 128.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 17 observations deleted due to missingness
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = Native_Dehumanize ~ Race, data = dfdehumanDV.race)
##
## $Race
## diff lwr upr p adj
## Black-Asian 0.3929971 -2.7582890 3.544283 0.9886075
## White-Asian 0.6937919 -1.8540521 3.241636 0.8970903
## Latino-Asian 4.1429585 0.3925199 7.893397 0.0235475
## White-Black 0.3007948 -1.7609760 2.362566 0.9820159
## Latino-Black 3.7499614 0.3111855 7.188737 0.0261964
## Latino-White 3.4491666 0.5532929 6.345040 0.0119136
This analysis is done via One way ANOVA using Dehumanization of Natives as the DV
Now, we’ll revisit the relationship between dehumanizing whites and support for the two holidays, this time broken down by race.
First Columbus Day:
## Warning: Removed 18 rows containing missing values (geom_point).
Then Indigenous Peoples’ Day:
## Warning: Removed 18 rows containing missing values (geom_point).
Now, onto the same analyses looking at Natives.
First Columbus Day:
## Warning: Removed 17 rows containing missing values (geom_point).
Then Indigenous Peoples’ Day:
## Warning: Removed 17 rows containing missing values (geom_point).
Now we’re going to take the opportunity to drill down a bit into the question of Native Stereotypes.
This time we’re going to pull the stereotype of Natives as Childlike as well as the stereotype of Natives as Uncivilized.
The way I’m going to do this is to pull both measures out of the dehumanization composite score and look at their effects on our DVs separately. To test this statistically we’ll place them into linear models together with the composite.
Let’s begin!
This first graph will show us regression lines for each of our 4 measures of dehumanization and their relationship with Support for abolishing Columbus Day
## Warning: Removed 416 rows containing missing values (geom_point).
## vars n mean sd median trimmed mad min
## SubjectNumber 1 2903 1452.00 838.17 1452.00 1452.00 1076.37 1
## No_Columbus 2 2858 3.79 2.13 4.00 3.74 2.97 1
## White_Dehumanize 3 2793 52.93 12.03 53.25 53.60 8.52 0
## Native_Dehumanize 4 2792 51.09 11.58 51.50 51.72 7.91 0
## DH5_W_Childlike 5 2809 40.76 24.49 43.00 39.98 26.69 0
## DH5_N_Childlike 6 2802 29.22 22.26 25.00 27.68 28.17 0
## max range skew kurtosis se
## SubjectNumber 2903.00 2902.00 0.00 -1.20 15.56
## No_Columbus 7.00 6.00 0.17 -1.31 0.04
## White_Dehumanize 99.95 99.95 -0.87 3.45 0.23
## Native_Dehumanize 100.00 100.00 -0.87 4.02 0.22
## DH5_W_Childlike 100.00 100.00 0.21 -0.55 0.46
## DH5_N_Childlike 100.00 100.00 0.54 -0.43 0.42
##
## Call:
## lm(formula = scale(No_Columbus, scale = F) ~ White_Dehumanize +
## Native_Dehumanize + DH5_W_Childlike + DH5_N_Childlike, data = dvstereotypes.child.c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8633 -1.8083 -0.0807 1.7581 4.6669
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.783234 0.188753 4.150 3.43e-05 ***
## White_Dehumanize -0.053827 0.004709 -11.431 < 2e-16 ***
## Native_Dehumanize 0.027957 0.004910 5.694 1.37e-08 ***
## DH5_W_Childlike 0.018252 0.001726 10.575 < 2e-16 ***
## DH5_N_Childlike -0.003985 0.001913 -2.082 0.0374 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.041 on 2781 degrees of freedom
## (117 observations deleted due to missingness)
## Multiple R-squared: 0.07825, Adjusted R-squared: 0.07693
## F-statistic: 59.03 on 4 and 2781 DF, p-value: < 2.2e-16
And we’ll do one for Indigenous Peoples’ Day as well:
## Warning: Removed 416 rows containing missing values (geom_point).
## vars n mean sd median trimmed mad min
## SubjectNumber 1 2903 1452.00 838.17 1452.00 1452.00 1076.37 1
## Create_IPD 2 2858 4.48 1.96 4.00 4.60 2.97 1
## White_Dehumanize 3 2793 52.93 12.03 53.25 53.60 8.52 0
## Native_Dehumanize 4 2792 51.09 11.58 51.50 51.72 7.91 0
## DH5_W_Childlike 5 2809 40.76 24.49 43.00 39.98 26.69 0
## DH5_N_Childlike 6 2802 29.22 22.26 25.00 27.68 28.17 0
## max range skew kurtosis se
## SubjectNumber 2903.00 2902.00 0.00 -1.20 15.56
## Create_IPD 7.00 6.00 -0.35 -0.97 0.04
## White_Dehumanize 99.95 99.95 -0.87 3.45 0.23
## Native_Dehumanize 100.00 100.00 -0.87 4.02 0.22
## DH5_W_Childlike 100.00 100.00 0.21 -0.55 0.46
## DH5_N_Childlike 100.00 100.00 0.54 -0.43 0.42
##
## Call:
## lm(formula = scale(Create_IPD, scale = F) ~ White_Dehumanize +
## Native_Dehumanize + DH5_W_Childlike + DH5_N_Childlike, data = dvstereotypes.child.i)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.5164 -1.3058 0.0292 1.6081 3.8440
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.033044 0.176129 -0.188 0.851
## White_Dehumanize -0.038655 0.004394 -8.798 < 2e-16 ***
## Native_Dehumanize 0.034383 0.004581 7.505 8.24e-14 ***
## DH5_W_Childlike 0.013216 0.001610 8.206 3.46e-16 ***
## DH5_N_Childlike -0.007674 0.001785 -4.298 1.78e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.905 on 2781 degrees of freedom
## (117 observations deleted due to missingness)
## Multiple R-squared: 0.05297, Adjusted R-squared: 0.05161
## F-statistic: 38.89 on 4 and 2781 DF, p-value: < 2.2e-16
Now we’ll take a look at stereotypes about being civilized using the same methods as before:
## Warning: Removed 411 rows containing missing values (geom_point).
## vars n mean sd median trimmed mad min
## SubjectNumber 1 2903 1452.00 838.17 1452.00 1452.00 1076.37 1
## No_Columbus 2 2858 3.79 2.13 4.00 3.74 2.97 1
## White_Dehumanize 3 2793 52.93 12.03 53.25 53.60 8.52 0
## Native_Dehumanize 4 2792 51.09 11.58 51.50 51.72 7.91 0
## DH2_W_Civilized 5 2810 65.89 23.38 69.00 67.81 25.20 0
## DH2_N_Civilized 6 2806 64.58 21.97 65.00 65.70 22.24 0
## max range skew kurtosis se
## SubjectNumber 2903.00 2902.00 0.00 -1.20 15.56
## No_Columbus 7.00 6.00 0.17 -1.31 0.04
## White_Dehumanize 99.95 99.95 -0.87 3.45 0.23
## Native_Dehumanize 100.00 100.00 -0.87 4.02 0.22
## DH2_W_Civilized 100.00 100.00 -0.69 0.20 0.44
## DH2_N_Civilized 100.00 100.00 -0.48 0.15 0.41
##
## Call:
## lm(formula = scale(No_Columbus, scale = F) ~ White_Dehumanize +
## Native_Dehumanize + DH2_W_Civilized + DH2_N_Civilized, data = dvstereotypes.civil.c)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9888 -1.7404 -0.0827 1.6746 4.8252
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.949289 0.188085 5.047 4.77e-07 ***
## White_Dehumanize -0.008293 0.005669 -1.463 0.1436
## Native_Dehumanize 0.011076 0.005481 2.021 0.0434 *
## DH2_W_Civilized -0.028291 0.002188 -12.932 < 2e-16 ***
## DH2_N_Civilized 0.012068 0.002126 5.676 1.52e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.019 on 2781 degrees of freedom
## (117 observations deleted due to missingness)
## Multiple R-squared: 0.09807, Adjusted R-squared: 0.09678
## F-statistic: 75.6 on 4 and 2781 DF, p-value: < 2.2e-16
And we’ll do one for Indigenous Peoples’ Day as well:
## Warning: Removed 411 rows containing missing values (geom_point).
## vars n mean sd median trimmed mad min
## SubjectNumber 1 2903 1452.00 838.17 1452.00 1452.00 1076.37 1
## Create_IPD 2 2858 4.48 1.96 4.00 4.60 2.97 1
## White_Dehumanize 3 2793 52.93 12.03 53.25 53.60 8.52 0
## Native_Dehumanize 4 2792 51.09 11.58 51.50 51.72 7.91 0
## DH2_W_Civilized 5 2810 65.89 23.38 69.00 67.81 25.20 0
## DH2_N_Civilized 6 2806 64.58 21.97 65.00 65.70 22.24 0
## max range skew kurtosis se
## SubjectNumber 2903.00 2902.00 0.00 -1.20 15.56
## Create_IPD 7.00 6.00 -0.35 -0.97 0.04
## White_Dehumanize 99.95 99.95 -0.87 3.45 0.23
## Native_Dehumanize 100.00 100.00 -0.87 4.02 0.22
## DH2_W_Civilized 100.00 100.00 -0.69 0.20 0.44
## DH2_N_Civilized 100.00 100.00 -0.48 0.15 0.41
##
## Call:
## lm(formula = scale(Create_IPD, scale = F) ~ White_Dehumanize +
## Native_Dehumanize + DH2_W_Civilized + DH2_N_Civilized, data = dvstereotypes.civil.i)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.530 -1.235 0.050 1.542 4.084
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.021525 0.176237 0.122 0.9028
## White_Dehumanize -0.009079 0.005312 -1.709 0.0875 .
## Native_Dehumanize 0.015670 0.005136 3.051 0.0023 **
## DH2_W_Civilized -0.018689 0.002050 -9.117 < 2e-16 ***
## DH2_N_Civilized 0.013673 0.001992 6.863 8.28e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.892 on 2781 degrees of freedom
## (117 observations deleted due to missingness)
## Multiple R-squared: 0.0656, Adjusted R-squared: 0.06425
## F-statistic: 48.81 on 4 and 2781 DF, p-value: < 2.2e-16